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Abdulmohsin Hammood, Dalal, Hasliza A. Rahim, Ahmed Alkhayyat, and R. Badlishah Ahmad. "Body-to-Body Cooperation in Internet of Medical Things: Toward Energy Efficiency Improvement." Future Internet 11, no. 11 (2019): 239. http://dx.doi.org/10.3390/fi11110239.
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Internet of Medical Things (IoMT) technologies provide suitability among physicians and patients because they are useful in numerous medical fields. Wireless body sensor networks (WBSNs) are one of the most crucial technologies from within the IoMT evolution of the healthcare system, whereby each patient is monitored by low-powered and lightweight sensors. When the WBSNs are integrated into IoMT networks, they are quite likely to overlap each other; thus, cooperation between WBSN sensors is possible. In this paper, we consider communication between WBSNs and beyond their communication range. Therefore, we propose inter-WBAN cooperation for the IoMT system, which is also known as inter-WBAN cooperation in an IoMT environment (IWC-IoMT). In this paper, first, a proposed architecture for the IoT health-based system is investigated. Then, a mathematical model of the outage probability for the IWC-IoMT is derived. Finally, the energy efficiency of the IWC-IoT is analysed and inspected. The simulation and numerical results show that the IWC-IoMT (cooperative IoMT) system provides superior performance compared to the non-cooperative system.
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Huang, Xucheng, and Shah Nazir. "Evaluating Security of Internet of Medical Things Using the Analytic Network Process Method." Security and Communication Networks 2020 (September 1, 2020): 1–14. http://dx.doi.org/10.1155/2020/8829595.
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Internet of Medical Things (IoMT) plays an important role in healthcare. Different devices such as smart sensors, wearable devices, handheld, and many other devices are connected in a network in the form of Internet of Things (IoT) for the smooth running of communication in healthcare. Security of these devices in healthcare is important due to its nature of functionality and efficiency. An efficient and robust security system is in dire need to cope with the attacks, threats, and vulnerability. The security evaluation of IoMT is an issue since couple of years. Therefore, the aim of the proposed study is to evaluate the security of IoMT by using the analytic network (ANP) process. The proposed approach is applied using ISO/IEC 27002 (ISO 27002) standard and some other important features from the literature. The results of the proposed research demonstrate the effective IoMT components which can further be used as secure IoMT.
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Zhao, Zhuo, Chingfang Hsu, Lein Harn, Qing Yang, and Lulu Ke. "Lightweight Privacy-Preserving Data Sharing Scheme for Internet of Medical Things." Wireless Communications and Mobile Computing 2021 (September 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/8402138.
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Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.
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Cano, Maria-Dolores, and Antonio Cañavate-Sanchez. "Preserving Data Privacy in the Internet of Medical Things Using Dual Signature ECDSA." Security and Communication Networks 2020 (June 10, 2020): 1–9. http://dx.doi.org/10.1155/2020/4960964.
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The disclosure of personal and private information is one of the main challenges of the Internet of Medical Things (IoMT). Most IoMT-based services, applications, and platforms follow a common architecture where wearables or other medical devices capture data that are forwarded to the cloud. In this scenario, edge computing brings new opportunities to enhance the operation of IoMT. However, despite the benefits, the inherent characteristics of edge computing require countermeasures to address the security and privacy issues that IoMT gives rise to. The restrictions of IoT devices in terms of battery, memory, hardware resources, or computing capabilities have led to a common agreement for the use of elliptic curve cryptography (ECC) with hardware or software implementations. As an example, the elliptic curve digital signature algorithm (ECDSA) is widely used by IoT devices to compute digital signatures. On the other hand, it is well known that dual signature has been an effective method to provide consumer privacy in classic e-commerce services. This article joins both approaches. It presents a novel solution to enhanced security and the preservation of data privacy in communications between IoMT devices and the cloud via edge computing devices. While data source anonymity is achieved from the cloud perspective, integrity and origin authentication of the collected data is also provided. In addition, computational requirements and complexity are kept to a minimum.
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Gautam, Kalpna, Vikram Puri, Jolanda G Tromp, Chung Van Le, and Nhu Gia Nguyen. "Internet of Things and Healthcare Technologies: A Valuable Synergy from Design to Implementation." International Journal of Machine Learning and Networked Collaborative Engineering 2, no. 3 (2018): 128–42. http://dx.doi.org/10.30991/ijmlnce.2018v02i03.005.
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Internet of Things (IoT) promises to be a reliable technology for the future. Healthcare is one of the fields which are rapidly developing new solutions. The synergy between IoT and healthcare promises to be very beneficial for human healthcare and evolved into a new field of research and development: the Internet of Medical Things (IoMT). This paper presents a review on various enabling IoMT technologies based on the latest publications and technology available in the marketplace. This article also analyzes the various software platforms available in the field of IoMT and the current challenges faced by the industry
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Koutras, Dimitris, George Stergiopoulos, Thomas Dasaklis, Panayiotis Kotzanikolaou, Dimitris Glynos, and Christos Douligeris. "Security in IoMT Communications: A Survey." Sensors 20, no. 17 (2020): 4828. http://dx.doi.org/10.3390/s20174828.
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The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.
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Mohammed, Sabah, Jinan Fiaidhi, and Sami Mohammed. "Internet of Medical Things (IOMT): Trends and Challenge." International Journal of Control and Automation 12, no. 3 (2019): 29–36. http://dx.doi.org/10.33832/ijca.2019.12.3.03.
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Mehmood, Iram, Sidra Anwar, AneezaDilawar, IsmaZulfiqar, and Raja Manzar Abbas. "Managing Data Diversity on the Internet of Medical Things (IoMT)." International Journal of Information Technology and Computer Science 12, no. 6 (2020): 49–56. http://dx.doi.org/10.5815/ijitcs.2020.06.05.
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In the healthcare industry, the Internet of Medical Services (IOMT) plays a vital role throughout the increasing performance, reliability, and efficiency of an electronic device. Healthcare is also characterized as being complicated due to its highly diverse and large number of shareholders. Data diversity refers to the continuum of various types of elements in the data. The integration of data is difficult where different sources can adopt different identification for the same entity, but there is no explicit connection. Researches are contributing to a digitized Health care system through interconnections available medical resources and health care services. This Research presents the contribution of IoT to people in the field of Healthcare, highlighting the issues in different data integration, analysis of the existing algorithms and models, applications, and future challenges of IoT in terms of healthcare medical services. Big data analytics that incorporates millions of fragmented, organized, and unstructured sources of data will play a key role in how health care will be delivered in the future.
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Kanthavel, R. "Review on Data Securing Techniques for Internet of Medical Things." September 2021 3, no. 3 (2021): 177–91. http://dx.doi.org/10.36548/jscp.2021.3.004.
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In recent days Internet of Things (IOT) has grown up dramatically. It has wide range of applications. One of its applications is Health care system. IOT helps in managing and optimizing of healthcare system. Though it helps in all ways it also brings security problem in account. There is lot of privacy issues aroused due to IOT. In some cases it leads to risk the patient’s life. To overcome this issue we need an architecture named Internet of Medical Things (IOMT). In this paper we have discussed the problems faced by healthcare system and the authentication approaches used by Internet of Medical Things. Machine learning approaches are used to improvise the system performance.
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Elsayeh, Muhammad, Kadry Ali Ezzat, Hany El-Nashar, and Lamia Nabil Omran. "CYBERSECURITY ARCHITECTURE FOR THE INTERNET OF MEDICAL THINGS AND CONNECTED DEVICES USING BLOCKCHAIN." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (2021): 2150013. http://dx.doi.org/10.4015/s1016237221500137.
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The internet of medical things (IoMT) has a great role in improving the health around the world. IoMT is having a great impact in our life in which the clinical data of the patient is observed and checked and then can be transferred to the third party for using in the future such as the cloud. IoMT is a huge data system with a continuous developing rate, which implies that we should keep a lot of data secure. We propose a combined security architecture that fuses the standard architecture and new blockchain technology. Blockchain is a temper digital ledger which gives peer-to-peer communication and provides communication between non-trust individuals. Using standard in-depth strategy and blockchain, we are able to develop a method to collect vital signs data from IoMT and connected devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion within a closed system, suitable for healthcare providers such as private clinics, hospitals, and healthcare organizations were sharing data with each other is required. Right now initially examine the innovation behind Blockchain then propose IoMT-based security architecture utilizing Blockchain to guarantee the security of information transmission between associated nodes. Experimental analysis shows that the proposed scheme presents a non-significant overhead; yet it brings major advantages to meet the standard security and privacy requirements in IoMT.
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Nikolaidou, Mara, Christos Kotronis, Ioannis Routis, et al. "Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study." Health Informatics Journal 27, no. 1 (2021): 146045822098264. http://dx.doi.org/10.1177/1460458220982640.
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Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.
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Ponomarev, K. Y., and A. A. Zaharov. "ACCESS CONTROL METHODS FOR INTERNET OF MEDICAL THINGS NETWORKS BASEDON ATTRIBUTE MODELS." Journal of the Ural Federal District. Information security 20, no. 4 (2020): 44–54. http://dx.doi.org/10.14529/secur200404.
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The term «Internet of Medical Things» (IoMT ) refers to a set of devices and technologies for remote monitoring of patients’ health using wearable devices. One primary problem with pa-tient’s data is ensuring privacy and resource intensive protection when it is transmitted over open communication channels and stored in cloud systems. However, when it comes to millions of IoT devices, technologies that have already become classic for Internet resources are not suit-able in many aspects at once: low computing power, out of memory, limited battery capacity and etc. The work considered Attribute-based encryption for ensuring security of personified data in IoMT networks. Also, the research studied the issues of patient’s data confidentiality in cloud systems, management of cryptographic keys and data sharing control. The algorithms for effective and secure solution were proposed. We have proposed a framework for processing patient data from portable diagnostic devices using ABE methods. The results of load testing of the prototype are presented too
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Hameed, Shilan S., Wan Haslina Hassan, Liza Abdul Latiff, and Fahad Ghabban. "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches." PeerJ Computer Science 7 (March 23, 2021): e414. http://dx.doi.org/10.7717/peerj-cs.414.
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Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.
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Gardašević, Gordana, Konstantinos Katzis, Dragana Bajić, and Lazar Berbakov. "Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare." Sensors 20, no. 13 (2020): 3619. http://dx.doi.org/10.3390/s20133619.
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Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.
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R Hanji, Bhagyashri. "Internet of Medical Things (IoMT) –Architecture, Benefits and Challenges." Journal of Information Technology and Sciences 6, no. 3 (2020): 1–6. http://dx.doi.org/10.46610/joits.2020.v06i03.001.
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Alsubaei, Faisal, Abdullah Abuhussein, Vivek Shandilya, and Sajjan Shiva. "IoMT-SAF: Internet of Medical Things Security Assessment Framework." Internet of Things 8 (December 2019): 100123. http://dx.doi.org/10.1016/j.iot.2019.100123.
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Wei, Kefeng, Lincong Zhang, and Shupeng Wang. "Intelligent Channel Allocation for Age of Information Optimization in Internet of Medical Things." Wireless Communications and Mobile Computing 2021 (August 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/6645803.
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Along with the development of realtime applications, the freshness of information becomes significant because the overdue information is worthless and useless and even harmful to the right judgement of system. Therefore, The Age of Information (AoI) used for marking the freshness of information is proposed. In Internet of Medical Things (IoMT), which is derived from the requirement of Internet of Thins (IoT) in medicine, high freshness of medical information should be guaranteed. In this paper, we introduce the AoI of medical information when allocating channels for users in IoMT. Due to the advantages of Deep Q-learning Network (DQN) applied in resource management in wireless network, we propose a novel DQN-based Channel Allocation (DQCA) algorithm to provide the strategy for channel allocation under the optimization of the system cost considering the AoI and energy consumption of coordinator nodes. Unlike the traditional centralized channel allocation methods, the DQCA algorithm is distributed as each user performs the DQN process separately. The simulation results show that our proposed DQCA algorithm is superior to Greedy algorithm and Q-learning algorithm in terms of the average AoI, average energy consumption and system cost.
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Lakhan, Abdullah, Mazhar Ali Dootio, Ali Hassan Sodhro, et al. "Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things." Mathematical Biosciences and Engineering 18, no. 6 (2021): 7344–62. http://dx.doi.org/10.3934/mbe.2021363.
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<abstract><p>These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.</p></abstract>
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Al-Shammari, N. K., T. H. Syed, and M. B. Syed. "An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications." Engineering, Technology & Applied Science Research 11, no. 4 (2021): 7326–31. http://dx.doi.org/10.48084/etasr.4245.
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The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.
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Qureshi, Fayez, and Sridhar Krishnan. "Wearable Hardware Design for the Internet of Medical Things (IoMT)." Sensors 18, no. 11 (2018): 3812. http://dx.doi.org/10.3390/s18113812.
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As the life expectancy of individuals increases with recent advancements in medicine and quality of living, it is important to monitor the health of patients and healthy individuals on a daily basis. This is not possible with the current health care system in North America, and thus there is a need for wireless devices that can be used from home. These devices are called biomedical wearables, and they have become popular in the last decade. There are several reasons for that, but the main ones are: expensive health care, longer wait times, and an increase in public awareness about improving quality of life. With this, it is vital for anyone working on wearables to have an overall understanding of how they function, how they were designed, their significance, and what factors were considered when the hardware was designed. Therefore, this study attempts to investigate the hardware components that are required to design wearable devices that are used in the emerging context of the Internet of Medical Things (IoMT). This means that they can be used, to an extent, for disease monitoring through biosignal capture. In particular, this review study covers the basic components that are required for the front-end of any biomedical wearable, and the limitations that these wearable devices have. Furthermore, there is a discussion of the opportunities that they create, and the direction that the wearable industry is heading in.
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Jahankhani, Hamid. "Digital Forensic Investigation For The Internet Of Medical Things (IoMT)." Journal of Forensic, Legal & Investigative Sciences 5, no. 2 (2019): 1–6. http://dx.doi.org/10.24966/flis-733x/100029.
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Magsi, Hina, Ali Hassan Sodhro, Mabrook S. Al-Rakhami, Noman Zahid, Sandeep Pirbhulal, and Lei Wang. "A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications." Electronics 10, no. 4 (2021): 367. http://dx.doi.org/10.3390/electronics10040367.
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The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.
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Sharma, Ashutosh, Sarishma, Ravi Tomar, Naveen Chilamkurti, and Byung-Gyu Kim. "Blockchain Based Smart Contracts for Internet of Medical Things in e-Healthcare." Electronics 9, no. 10 (2020): 1609. http://dx.doi.org/10.3390/electronics9101609.
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The concept of Blockchain has penetrated a wide range of scientific areas, and its use is considered to rise exponentially in the near future. Executing short scripts of predefined code called smart contracts on Blockchain can eliminate the need of intermediaries and can also raise the multitude of execution of contracts. In this paper, we discuss the concept of Blockchain along with smart contracts and discuss their applicability in the Internet of Medical Things (IoMT) in the e-healthcare domain. The paper analyses the dimensions that decentralization and the use of smart contracts will take the IoMT in e-healthcare, proposes a novel architecture, and also outlines the advantages, challenges, and future trends related to the integration of all three. The proposed architecture shows its effectiveness with average packet delivery ratio, average latency, and average energy efficiency performance parameters when compared with traditional approaches.
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Santana-Mancilla, Pedro C., Luis E. Anido-Rifón, Juan Contreras-Castillo, and Raymundo Buenrostro-Mariscal. "Heuristic Evaluation of an IoMT System for Remote Health Monitoring in Senior Care." International Journal of Environmental Research and Public Health 17, no. 5 (2020): 1586. http://dx.doi.org/10.3390/ijerph17051586.
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This paper presents the usability assessment of the design of an Internet of Medical Things (IoMT) system for older adults; the evaluation, using heuristics, was held early on the design process to assess potential problems with the system and was found to be an efficient method to find issues with the application design and led to significant usability improvements on the IoMT platform.
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Arora, Saanvi. "IoMT (Internet of Medical Things): Reducing Cost While Improving Patient Care." IEEE Pulse 11, no. 5 (2020): 24–27. http://dx.doi.org/10.1109/mpuls.2020.3022143.
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AlShorman, Omar, Buthaynah AlShorman, Mahmood Al-khassaweneh, and Fahad Alkahtani. "A review of internet of medical things (IoMT) - based remote health monitoring through wearable sensors: a case study for diabetic patients." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 414. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp414-422.
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<span>The latest advances and trends in information technology and communication have a vital role in healthcare industries. </span><span>Theses advancements led to the Internet of Medical Things (IoMT) which provides a continuous, remote and real-time monitoring of patients. The IoMT </span><span>architectures still face many challenges related to the bandwidth, communication protocols, big data and data volume, flexibility, reliability, data management, data acquisition, data processing and analytics availability, cost effectiveness, data security and privacy, and energy efficiency. The goal of this paper is to find </span><span>feasible </span><span>solutions to enhance the healthcare living facilities using remote health monitoring (RHM) and IoMT. In addition, the enhancement of the prevention, prognosis, diagnosis and treatment abilities using IoMT and RHM is also discussed. </span><span>A case study of monitoring the vital signs of diabetic patients using real-time data processing and IoMT is also presented</span><span>. </span>
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Makarenko, M. V. "FEATURES OF INTRODUCTION OF INTERNET OF THINGS TECHNOLOGIES (INTERNET OF THINGS, IoT; INTERNET OF MEDICAL THINGS, IoMT) IN THE FIELD OF HEALTHCARE." "Scientific Notes of Taurida V.I. Vernadsky University", series "Public Administration", no. 2 (2021): 64–68. http://dx.doi.org/10.32838/tnu-2663-6468/2021.2/011.
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Makarenko, M. V. "FEATURES OF INTRODUCTION OF INTERNET OF THINGS TECHNOLOGIES (INTERNET OF THINGS, IoT; INTERNET OF MEDICAL THINGS, IoMT) IN THE FIELD OF HEALTHCARE." "Scientific Notes of Taurida V.I. Vernadsky University", series "Public Administration", no. 2 (2021): 64–68. http://dx.doi.org/10.32838/tnu-2663-6468/2021.2/11.
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Alam, Mahbub Ul, and Rahim Rahmani. "Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application." Sensors 21, no. 15 (2021): 5025. http://dx.doi.org/10.3390/s21155025.
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Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.
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Vaccari, Ivan, Vanessa Orani, Alessia Paglialonga, Enrico Cambiaso, and Maurizio Mongelli. "A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data." Sensors 21, no. 11 (2021): 3726. http://dx.doi.org/10.3390/s21113726.
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The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.
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Wang, Na, Yuanyuan Cai, Junsong Fu, and Jie Xu. "Privacy-Preserving Efficient Data Retrieval in IoMT Based on Low-Cost Fog Computing." Complexity 2021 (June 21, 2021): 1–13. http://dx.doi.org/10.1155/2021/6211475.
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The rapid development of Internet of Medical Things (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
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Basatneh, Rami, Bijan Najafi, and David G. Armstrong. "Health Sensors, Smart Home Devices, and the Internet of Medical Things: An Opportunity for Dramatic Improvement in Care for the Lower Extremity Complications of Diabetes." Journal of Diabetes Science and Technology 12, no. 3 (2018): 577–86. http://dx.doi.org/10.1177/1932296818768618.
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Objective: The prevalent and long-neglected diabetic foot ulcer (DFU) and its related complications rank among the most debilitating and costly sequelae of diabetes. With the rise of the Internet of medical things (IoMT), along with smart devices, the med-tech industry is on the cusp of a home-care revolution, which could also create opportunity for developing effective solutions with significant potential to reduce DFU-associated costs and saving limbs. This article discusses potential applications of IoMT to the DFU patient population and beyond. Methods: To better understand potential opportunities and challenges associated with implementing IoMT for management of DFU, the authors reviewed recent relevant literatures and included their own expert opinions from a multidisciplinary point of view including podiatry, engineering, and data security. Results: The IoMT has opened digital transformation of home-based diabetic foot care, as it enables promoting patient engagement, personalized care and smart management of chronic and noncommunicable diseases through individual data-driven treatment regimens, telecommunication, data mining, and comprehensive feedback tailored to individual requirements. In particular, with recent advances in voice-activated commands technology and its integration as a part of IoMT, new opportunities have emerged to improve the patient’s central role and responsibility in enabling an optimized health care ecosystem. Conclusions: The IoMT has opened new opportunities in health care from remote monitoring to smart sensors and medical device integration. While it is at its early stage of development, ultimately we envisage a connected home that, using voice-controlled technology and Bluetooth-radio-connected add-ons, may augment much of what home health does today.
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Estrela, Vania V. "SDR-Based High-Definition Video Transmission for Biomedical Engineering." Medical Technologies Journal 4, no. 3 (2020): 584–85. http://dx.doi.org/10.26415/2572-004x-vol4iss3p584-585.
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Background: Software-Defined Radio (SDR) frameworks from cellular telephone base stations, e.g., Multiservice Distributed Access System (MDAS) and small cells, employ extensively integrated RF agile transceivers. The Internet of Medical Things (IoMT) is the collection of medical devices and applications that connect to healthcare IT systems through online computer networks. Medical devices equipped with Wi-Fi allow M2M communication, which is the backbone of IoMT and associated devices linked to cloud platforms containing stored data to be analyzed. Examples of IoMT include remote patient monitoring of people with chronic or long-term conditions, tracking patient medication orders and the location of patients admitted to hospitals, and patients' wearables to send info to caregivers. Infusion pumps connected to dashboards and hospital beds rigged with sensors measuring patients' vital signs are medical devices that can be converted to or deployed as IoMT technology.
 Methods: This work proposes an SDR architecture to allow wireless High-Definition (HD) video broadcast for biomedical applications. This text examines a Wideband Wireless Video (WWV) signal chain implementation using the transceivers, the data transmitted volume, the matching occupied RF bandwidth, the communication distance, the transmitter’s power, and the implementation of the PHY layer as Orthogonal Frequency Division Multiplexing (OFDM) with test results to evade RF interference.
 Results: As the IoMT grows, the amount of possible IoMT uses increases. Many mobile devices employ Near Field Communication (NFC) Radio Frequency Identification (RFID) tags allowing them to share data with IT systems. RFID tags in medical equipment and supplies allow hospital staff can remain aware of the quantities they have in stock. The practice of using IoMT devices to observe patients in their homes remotely is also known as telemedicine. This kind of treatment spares patients from traveling to healthcare facilities whenever they have a medical question or change in their condition.
 Conclusion: An SDR-based HD biomedical video transmission is proposed, with its benefits and disadvantages for biomedical WWV are discussed. The security of IoMT sensitive data is a developing concern for healthcare providers.
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Han, Bo, Zhao Yin-Liang, and Zhu Chang-Peng. "An Object Proxy-Based Dynamic Layer Replacement to Protect IoMT Applications." Security and Communication Networks 2019 (September 30, 2019): 1–9. http://dx.doi.org/10.1155/2019/2798571.
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The Internet of medical things (IoMT) has become a promising paradigm, where the invaluable additional data can be collected by the ordinary medical devices when connecting to the Internet. The deep understanding of symptoms and trends can be provided to patients to manage their lives and treatments. However, due to the diversity of medical devices in IoMT, the codes of healthcare applications may be manipulated and tangled by malicious devices. In addition, the linguistic structures for layer activation in languages cause controls of layer activation to be part of program’s business logic, which hinders the dynamic replacement of layers. Therefore, to solve the above critical problems in IoMT, in this paper, a new approach is firstly proposed to support the dynamic replacement of layer in IoMT applications by incorporating object proxy into virtual machine (VM). Secondly, the heap and address are used to model the object and object evolution to guarantee the feasibility of the approach. After that, we analyze the influences of field access and method invocation and evaluate the risk and safety of the application when these constraints are satisfied. Finally, we conduct the evaluations by extending Java VM to validate the effectiveness of the proposal.
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Mavrogiorgou, Argyro, Athanasios Kiourtis, Marios Touloupou, Evgenia Kapassa, and Dimosthenis Kyriazis. "Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing." Emerging Science Journal 3, no. 2 (2019): 64. http://dx.doi.org/10.28991/esj-2019-01170.
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The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis.
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Jan, Saeed Ullah, Sikandar Ali, Irshad Ahmed Abbasi, Mogeeb A. A. Mosleh, Ahmed Alsanad, and Hizbullah Khattak. "Secure Patient Authentication Framework in the Healthcare System Using Wireless Medical Sensor Networks." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–20. http://dx.doi.org/10.1155/2021/9954089.
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Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respiratory rate, electroencephalogram (EEG), electrocardiogram (ECG), and blood pressure. Such transmission is performed via Wireless Medical Sensor Network (WMSN) while diagnosing patients remotely through Internet-of-Medical-Things (IoMT). The sensitive data transmitted through WMSN from IoMT over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. In contrast to addressing the security of all associated entities involving patient monitoring in the healthcare system or ensuring the integrity, authorization, and nonrepudiation of information over the communication line, no one can guarantee its security without a robust authentication protocol. Therefore, we have proposed a lightweight and robust authentication scheme for the network-enabled healthcare devices (IoMT) that mitigate all the identified weaknesses posed in the recent literature. The proposed protocol’s security has been analyzed formally using BAN logic and ProVerif2.02 and informally using pragmatic illustration. Simultaneously, at the end of the paper, the performance analysis result shows a delicate balance of security with performance that is often missing in the current protocols.
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Liaqat, Shahzana, Adnan Akhunzada, Fatema Sabeen Shaikh, Athanasios Giannetsos, and Mian Ahmad Jan. "SDN orchestration to combat evolving cyber threats in Internet of Medical Things (IoMT)." Computer Communications 160 (July 2020): 697–705. http://dx.doi.org/10.1016/j.comcom.2020.07.006.
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Kotronis, Christos, Ioannis Routis, Elena Politi, et al. "Evaluating Internet of Medical Things (IoMT)-Based Systems from a Human-Centric Perspective." Internet of Things 8 (December 2019): 100125. http://dx.doi.org/10.1016/j.iot.2019.100125.
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NATHANI, NEETA, and Zohaib Hasan. "IMPACT OF AI IN INTERNET OF MEDICAL THINGS FOR HEALTH CARE DELIVERY." International Journal of Engineering Technologies and Management Research 8, no. 8 (2021): 18–26. http://dx.doi.org/10.29121/ijetmr.v8.i8.2021.1012.
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The Internet of Things (IoT) is a network of wireless, interconnected, and networked digital devices that can gather, send, and store data without the need for human or computer interaction. The Internet of Things has a lot of promise for expediting and improving health care delivery by proactively predicting health issues and diagnosing, treating, and monitoring patients both in and out of the hospital. Understanding how established and emerging IoT technologies may help health systems deliver safe and effective treatment is becoming increasingly critical. The purpose of this viewpoint paper is to present an overview of existing IoT technology in health care, as well as to describe how IoT devices are improving health service delivery and how IoT technology can alter and disrupt global healthcare in the next decade. The promise of IoT-based health care is explored further to theorize how IoT can increase access to preventative public health services and help us migrate from our existing secondary and tertiary health care systems to a more proactive, continuous, and integrated approach. The intersection of the Internet of Medical Things (IoMT) for patient monitoring and chronic care management and the use of Artificial Intelligence (AI) is becoming more promising than ever as the adoption of telemedicine continues to grow dramatically. Connected devices generate huge volumes of data based on real-time measurements of patient vitals, which is delivered to cloud-based applications that are monitored by medical specialists in virtual contact centres. The policy is applied per-patient, and healthcare providers receive warnings and messages when a patient's heart rate, oxygen level, glucose level, blood pressure, or other measurement reaches a set threshold. Depending on the sort of telemedicine and telehealth platforms in use, this data is tracked and acted upon by specialists who monitor many patients for many different practices, and in other circumstances, this data is sent directly to the provider. AI in healthcare, as well as other crucial technologies are essential for resolving the issue and producing future prosperity.
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Kim, Taehoon, Wonbin Kim, Daehee Seo, and Imyeong Lee. "Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments." Sensors 21, no. 10 (2021): 3474. http://dx.doi.org/10.3390/s21103474.
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Recently, as Internet of Things systems have been introduced to facilitate diagnosis and treatment in healthcare and medical environments, there are many issues concerning threats to these systems’ security. For instance, if a key used for encryption is lost or corrupted, then ciphertexts produced with this key cannot be decrypted any more. Hence, this paper presents two schemes for key recovery systems that can recover the lost or the corrupted keys of an Internet of Medical Things. In our proposal, when the key used for the ciphertext is needed, this key is obtained from a Key Recovery Field present in the cyphertext. Thus, the recovered key will allow decrypting the ciphertext. However, there are threats to this proposal, including the case of the Key Recovery Field being forged or altered by a malicious user and the possibility of collusion among participating entities (Medical Institution, Key Recovery Auditor, and Key Recovery Center) which can interpret the Key Recovery Field and abuse their authority to gain access to the data. To prevent these threats, two schemes are proposed. The first one enhances the security of a multi-agent key recovery system by providing the Key Recovery Field with efficient integrity and non-repudiation functions, and the second one provides a proxy re-encryption function resistant to collusion attacks against the key recovery system.
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Lakhan, Abdullah, Mazin Abed Mohammed, Ahmed N. Rashid, et al. "Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System." Sensors 21, no. 12 (2021): 4093. http://dx.doi.org/10.3390/s21124093.
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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.
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S. Rubí, Jesús N., and Paulo R. L. Gondim. "IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR." Sensors 19, no. 19 (2019): 4283. http://dx.doi.org/10.3390/s19194283.
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Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).
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Vinoth, B., M. Ramaswami, and P. Swathika. "Internet of Medical Things (IoMT) using Hybrid Security and Near Field Communication (NFC) Technology." International Journal of Computer Applications 174, no. 7 (2017): 37–40. http://dx.doi.org/10.5120/ijca2017915435.
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Ahmed, Imran, Eulalia Balestrieri, and Francesco Lamonaca. "IoMT-based biomedical measurement systems for healthcare monitoring: a review." ACTA IMEKO 10, no. 2 (2021): 174. http://dx.doi.org/10.21014/acta_imeko.v10i2.1080.
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<p class="Abstract"><span lang="EN-US">Biomedical measurement systems (BMS) have provided new solutions for healthcare monitoring and the diagnosis of various chronic diseases. With a growing demand for BMS in the field of medical applications, researchers are focusing on advancing these systems, including Internet of Medical Things (IoMT)-based BMS, with the aim of improving bioprocesses, healthcare systems and technologies for biomedical equipment. This paper presents an overview of recent activities towards the development of IoMT-based BMS for various healthcare applications. Different methods and approaches used in the development of these systems are presented and discussed, taking into account some metrological aspects related to the requirement for accuracy, reliability and calibration. The presented IoMT-based BMS are applied to healthcare applications concerning, in particular, heart, brain and blood sugar diseases as well as internal body sound and blood pressure measurements. Finally, the paper provides a discussion about the shortcomings and challenges that need to be addressed along with some possible directions for future research activities.</span></p>
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Tariq, Muhammad Imran, Natash Ali Mian, Abid Sohail, Tahir Alyas, and Rehan Ahmad. "Evaluation of the Challenges in the Internet of Medical Things with Multicriteria Decision Making (AHP and TOPSIS) to Overcome Its Obstruction under Fuzzy Environment." Mobile Information Systems 2020 (August 26, 2020): 1–19. http://dx.doi.org/10.1155/2020/8815651.
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The exponential speed of advancement of innovation has expanded the needs of all users to avail all their information on the Internet 24/7. The Internet of things (IoT) enables smart objects to develop a significant building block in the development of the pervasive framework. The messaging between objects with one another means the least work and least expense for the enterprise. The industry that intends to implement the Internet of medical things (IoMT) in its organizations is still facing difficulties. Recognition and solving of these challenges are a time-consuming task and also need significant expenses if not adequately evaluated and prioritized. The application of the Internet of things is covered in almost every area, including medical/healthcare. In this research, the authors investigated the factors dealing with the Internet of medical things. The outcome of this study is to prioritize the level of significance of the elements causing these challenges, evaluated through fuzzy logic and multicriteria decision-making (MCDM) techniques like Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP). It would be beneficial for enterprises to save time and revenue. The main criteria, as well as subcriteria, were determined after due consultation with the Internet of medical things experts. In this study, our goals are to figure out which criteria/factors create hurdles in the adoption of the Internet of medical things. Through the investigation, we figured out 20 criteria ought to be given more importance/preference by the industry that is in the transition phase of the Internet of medical things adoption. The enterprise, with the help of this study, will be enabled to accelerate that adoption by limiting time and fiscal misfortune.
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Pandi, Suganya, and Pradeep Reddy Ch. "PMCAR: proactive mobility and congestion aware route prediction mechanism in IoMT for delay sensitive medical applications to ensure reliability in COVID-19 pandemic situation." International Journal of Pervasive Computing and Communications 16, no. 5 (2020): 429–46. http://dx.doi.org/10.1108/ijpcc-06-2020-0061.
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Purpose Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption. Design/methodology/approach In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service. Findings The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications. Originality/value The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.
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Ersotelos, Nikolaos, Mirko Bottarelli, Haider Al-Khateeb, et al. "Blockchain and IoMT against Physical Abuse: Bullying in Schools as a Case Study." Journal of Sensor and Actuator Networks 10, no. 1 (2020): 1. http://dx.doi.org/10.3390/jsan10010001.
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By law, schools are required to protect the well-being of students against problems such as on-campus bullying and physical abuse. In the UK, a report by the Office for Education (OfE) showed 17% of young people had been bullied during 2017–2018. This problem continues to prevail with consequences including depression, anxiety, suicidal thoughts, and eating disorders. Additionally, recent evidence suggests this type of victimisation could intensify existing health complications. This study investigates the opportunities provided by Internet of Medical Things (IoMT) data towards next-generation safeguarding. A new model is developed based on blockchain technology to enable real-time intervention triggered by IoMT data that can be used to detect stressful events, e.g., when bullying takes place. The model utilises private permissioned blockchain to manage IoMT data to achieve quicker and better decision-making while revolutionising aspects related to compliance, double-entry, confidentiality, and privacy. The feasibility of the model and the interaction between the sensors and the blockchain was simulated. To facilitate a close approximation of an actual IoMT environment, we clustered and decomposed existing medical sensors to their attributes, including their function, for a variety of scenarios. Then, we demonstrated the performance and capabilities of the emulator under different loads of sensor-generated data. We argue to the suitability of this emulator for schools and medical centres to conduct feasibility studies to address sensor data with disruptive data processing and management technologies.
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Hwang, Yong-Woon, and Im-Yeong Lee. "A Study on CP-ABE-Based Medical Data Sharing System with Key Abuse Prevention and Verifiable Outsourcing in the IoMT Environment." Sensors 20, no. 17 (2020): 4934. http://dx.doi.org/10.3390/s20174934.
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Recent developments in cloud computing allow data to be securely shared between users. This can be used to improve the quality of life of patients and medical staff in the Internet of Medical Things (IoMT) environment. However, in the IoMT cloud environment, there are various security threats to the patient’s medical data. As a result, security features such as encryption of collected data and access control by legitimate users are essential. Many studies have been conducted on access control techniques using ciphertext-policy attribute-based encryption (CP-ABE), a form of attribute-based encryption, among various security technologies and studies are underway to apply them to the medical field. However, several problems persist. First, as the secret key does not identify the user, the user may maliciously distribute the secret key and such users cannot be tracked. Second, Attribute-Based Encryption (ABE) increases the size of the ciphertext depending on the number of attributes specified. This wastes cloud storage, and computational times are high when users decrypt. Such users must employ outsourcing servers. Third, a verification process is needed to prove that the results computed on the outsourcing server are properly computed. This paper focuses on the IoMT environment for a study of a CP-ABE-based medical data sharing system with key abuse prevention and verifiable outsourcing in a cloud environment. The proposed scheme can protect the privacy of user data stored in a cloud environment in the IoMT field, and if there is a problem with the secret key delegated by the user, it can trace a user who first delegated the key. This can prevent the key abuse problem. In addition, this scheme reduces the user’s burden when decoding ciphertext and calculates accurate results through a server that supports constant-sized ciphertext output and verifiable outsourcing technology. The goal of this paper is to propose a system that enables patients and medical staff to share medical data safely and efficiently in an IoMT environment.
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Wei, Kefeng, Lincong Zhang, Xin Jiang, and Yi Guo. "Q -Learning-Based High Credibility and Stability Routing Algorithm for Internet of Medical Things." Wireless Communications and Mobile Computing 2020 (December 26, 2020): 1–10. http://dx.doi.org/10.1155/2020/8856271.
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With the outbreak of COVID-19, people’s demand for using the Internet of Medical Things (IoMT) for physical health monitoring has increased dramatically. The considerable amount of data requires stable, reliable, and real-time transmission, which has become an urgent problem to be solved. This paper constructs a health monitoring-enabled IoMT network which is composed of several users carrying wearable devices and a coordinator. One of the important problems for the proposed network is the unstable and inefficient transmission of data packets caused by node congestion and link breakage in the routing process. Based on these, we propose a Q -learning-based dynamic routing selection (QDRS) algorithm. First, a mathematical model of path optimization and a solution named Global Routing selection with high Credibility and Stability (GRCS) is proposed to select the optimal path globally. However, during the data transmission through the optimal path, the node and link status may change, causing packet loss or retransmission. This is a problem not considered by standard routing algorithms. Therefore, this paper proposes a local link dynamic adjustment scheme based on GRCS, using the Q -learning algorithm to select the optimal next-hop node for each intermediate forwarding node. If the selected node is not the same as the original path, the chosen node replaces the downstream node in the original path and so corrects the optimal path in time. This paper considers the congestion state, remaining energy, and mobility of the node when selecting the path and considers the network state changes during packet transmission, which is the most significant innovation of this paper. The simulation results show that compared with other similar algorithms, the proposed algorithm can significantly improve the packet forwarding rate without seriously affecting the network energy consumption and delay.
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Silva, André F., and Mahmoud Tavakoli. "Domiciliary Hospitalization through Wearable Biomonitoring Patches: Recent Advances, Technical Challenges, and the Relation to Covid-19." Sensors 20, no. 23 (2020): 6835. http://dx.doi.org/10.3390/s20236835.
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This article reviews recent advances and existing challenges for the application of wearable bioelectronics for patient monitoring and domiciliary hospitalization. More specifically, we focus on technical challenges and solutions for the implementation of wearable and conformal bioelectronics for long-term patient biomonitoring and discuss their application on the Internet of medical things (IoMT). We first discuss the general architecture of IoMT systems for domiciliary hospitalization and the three layers of the system, including the sensing, communication, and application layers. In regard to the sensing layer, we focus on current trends, recent advances, and challenges in the implementation of stretchable patches. This includes fabrication strategies and solutions for energy storage and energy harvesting, such as printed batteries and supercapacitors. As a case study, we discuss the application of IoMT for domiciliary hospitalization of COVID 19 patients. This can be used as a strategy to reduce the pressure on the healthcare system, as it allows continuous patient monitoring and reduced physical presence in the hospital, and at the same time enables the collection of large data for posterior analysis. Finally, based on the previous works in the field, we recommend a conceptual IoMT design for wearable monitoring of COVID 19 patients.